Atomistic Macroscopic Simulations of Collisional Plasmas

碰撞等离子体的原子宏观模拟

• 批准号: 2108505
• 负责人: Michael Murillo
• 金额: $ 50万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2108505&HistoricalAwards=false
• 关键词: Atomistic; Macroscopic; Simulations; Collisional; Plasmas

This project will use high performance computing to simulate small amounts of very hot matter down to the level of individual atoms, using machine learning to advance current capabilities. Typical mathematical models of matter are approximate because it would almost always take too much time and computing power to solve equations that describe what every individual atom does. This project will push through that barrier by developing methods to simulate small pieces of matter, including literally every individual atom. The goal is to provide the most accurate description of matter possible. The focus is on extremely hot matter -- plasmas -- which creates unique challenges relative to other forms of matter. An important aspect of this new approach to simulating matter is that the simulations will observe themselves and learn from their own mistakes, so that their performance continuously improves by this process of "machine learning." Machine learning will enable advances that have never been possible in this area of research. The capabilities developed in this project will be useful in future fundamental and applied research of societal relevance, including industrial and national defense applications, and could lead to advances ranging from understanding how stars age to manufacturing more powerful computer chips to protecting communication satellites.A deeper understanding of non-equilibrium, heterogeneous non-ideal plasmas will be developed to allow for the development of improved molecular dynamics (MD) models. Historically, the computational cost of MD has severely restricted its use to simulations of very small numbers of particles, typically only 100s to 1000s, with simulations relying on periodicity to mimic a bulk material. This has restricted modeling to mostly equilibrium, homogeneous plasma physics. Recent advances in computational methods and machine learning offer avenues to break through this barrier. As the accessible scales increase, so do important gradients and entirely new modeling issues appear. Force laws are the key input to MD, and scientists are now at a juncture where the problems they can tackle with MD cannot use force laws developed in the past. Further, unique to plasmas, gradients introduce new physical effects such as mesoscopic electric fields that can enhance transport, while the potential increase in computational cost associated with gradients can be severe. In this project, both computational models and machine learning techniques for non-uniform plasmas will be obtained to address classes of problems previously beyond the reach of MD. Using these new methods, mesoscopic plasma instabilities will be atomistically explored in the presence of electric fields and transport.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将使用高性能计算来模拟少量非常热的物质，直到单个原子的水平，使用机器学习来提高当前的能力。 典型的物质数学模型是近似的，因为它几乎总是需要太多的时间和计算能力来求解描述每个原子的方程。 这个项目将通过开发模拟小块物质的方法来突破这一障碍，包括每一个原子。目的是尽可能提供最准确的物质描述。重点是极热的物质-等离子体-相对于其他形式的物质，它创造了独特的挑战。这种模拟物质的新方法的一个重要方面是，模拟将观察自己并从自己的错误中学习，以便通过这个“机器学习”过程不断提高它们的性能。“机器学习将使这一研究领域取得前所未有的进步。 该项目开发的能力将有助于未来的社会相关性基础和应用研究，包括工业和国防应用，并可能导致从了解恒星如何老化到制造更强大的计算机芯片到保护通信卫星的进步。异质非理想等离子体将被开发，以允许改进的分子动力学（MD）模型的发展。 从历史上看，MD的计算成本严重限制了其用于模拟非常小数量的粒子，通常只有100到1000个，模拟依赖于周期性来模拟块状材料。 这限制了建模主要是平衡，均匀等离子体物理。计算方法和机器学习的最新进展为突破这一障碍提供了途径。随着可访问比例的增加，重要的渐变和全新的建模问题也会出现。力定律是MD的关键输入，科学家们现在正处于一个关键时刻，他们可以用MD解决的问题不能使用过去开发的力定律。此外，等离子体特有的梯度引入了新的物理效应，例如可以增强传输的介观电场，而与梯度相关的计算成本的潜在增加可能是严重的。在这个项目中，将获得非均匀等离子体的计算模型和机器学习技术，以解决以前MD无法解决的问题。使用这些新方法，介观等离子体不稳定性将在电场和运输的存在下进行原子探索。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。